Explanation: Tuning indexing parameters ensures an optimal trade -off between accuracy and speed. Reducing query complexity, avoiding transformations, and limiting distance calculations negatively impact robustness. Which retrieval method improves the relevance of results when integrating a Python -based RAG system with Oracle AI Vector Search?
C. DBMS_AI_VECTOR.GET_CONTEXTUAL_MATCH(:1, :2, :3, :4) D. DBMS_AI_TEXT.EXECUTE_VECTOR_QUERY(:1, :2, :3, :4) Explanation: The DBMS_AI_SEARCH.SEARCH_SIMILARITY function is used to retrieve similar vector embeddings from the database, helping to fetch relevant documents in a RAG pipeline. The other functions do not exist, making them incorrect options for vector similarity searches. What is a major challenge when loading high -dimensional vector data with SQL Loader?
Which vector distance metric is best suited for categorical embeddings?
A higher nprobe value means more partitions are searched, increasing accuracy but also computational cost. It does not reduce the number of partitions, enforce exact matches, or reduce memory footprint, as vector storage remains unchanged. Which indexing method optimizes vector search performance in a Python -based Oracle RAG system?
In Oracle Database 23ai, which data type is used to store vector embeddings for similarity search?
Explanation: When inserting vectors, the dimensionality must match the column definition. Unique constraints are optional, indexing is performed after insertion, and foreign key references are not a requirement for vector storage. Which indexing method is commonly used in Oracle AI Vector Search to optimize vector similarity queries?
